# Library should be pre-installed: opencv(opencv_python-4.0.1+contrib-cp36-cp36m-win_amd64.whl)
# Python version used: 3.6.6 64bit
# Opencv version used: 4.4.0.40
# IDE used: Visual Studio Code 1.31.1 
# Ref URL1(yolo-fastest): https://github.com/dog-qiuqiu/Yolo-Fastest

import cv2 #pip install opencv-python
import numpy as np
import numpy as np
import cv2 as cv #pip install opencv-python==4.4.0.40 -i https://pypi.douban.com/simple --user
import os
import time

yolo_dir = 'E:/Work/Product/4.Interactive_Art_AI/1.Python_Prj/tensorflow/yolo/yolo_fastest_XL'  # YOLO V3 fastest文件路径
data_dir = 'E:/Work/Product/4.Interactive_Art_AI/1.Python_Prj/tensorflow/yolo/data'
weightsPath = os.path.join(yolo_dir, 'yolo-fastest-xl.weights')  # 权重文件
configPath = os.path.join(yolo_dir, 'yolo-fastest-xl.cfg')  # 配置文件
labelsPath = os.path.join(data_dir, 'coco.names')  # label名称
imgPath = os.path.join(data_dir, 'person.jpg')  # 测试图像
CONFIDENCE = 0.85  # 过滤弱检测的最小概率
THRESHOLD = 0.4  # 非最大值抑制阈值

print(cv.__version__)
# 加载网络、配置权重
net = cv.dnn.readNetFromDarknet(configPath, weightsPath)  # #  利用下载的文件
print("[INFO] loading YOLO-Fastest-XL from disk...")  # # 可以打印下信息

cv2.namedWindow("YOLO-Fastest-XL Test") # Create a window
cap = cv2.VideoCapture(0) #Open camera one
success, frame = cap.read() #Read one frame

print("Camera open operation is: ", success)
color = (255,0,0) #Config the color

while success:
    success, frame = cap.read()
    size = frame.shape[:2] #
    image = np.zeros(size, dtype = np.float16) #
    image = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) #
    cv2.equalizeHist(image, image) #
    
    #Doing YOLO3
    # 加载图片、转为blob格式、送入网络输入层
    img = frame.copy()
    blobImg = cv.dnn.blobFromImage(img, 1.0/255.0, (416, 416), None, True, False)   # # net需要的输入是blob格式的，用blobFromImage这个函数来转格式
    net.setInput(blobImg)  # # 调用setInput函数将图片送入输入层

    # 获取网络输出层信息（所有输出层的名字），设定并前向传播
    outInfo = net.getUnconnectedOutLayersNames()  # # 前面的yolov3架构也讲了，yolo在每个scale都有输出，outInfo是每个scale的名字信息，供net.forward使用
    start = time.time()
    layerOutputs = net.forward(outInfo)  # 得到各个输出层的、各个检测框等信息，是二维结构。
    end = time.time()
    print("[INFO] YOLO-Fastest-XL took {:.6f} seconds".format(end - start))  # # 可以打印下信息

    # 拿到图片尺寸
    (H, W) = img.shape[:2]
    # 过滤layerOutputs
    # layerOutputs的第1维的元素内容: [center_x, center_y, width, height, objectness, N-class score data]
    # 过滤后的结果放入：
    boxes = [] # 所有边界框（各层结果放一起）
    confidences = [] # 所有置信度
    classIDs = [] # 所有分类ID

    # # 1）过滤掉置信度低的框框
    for out in layerOutputs:  # 各个输出层
        for detection in out:  # 各个框框
            # 拿到置信度
            scores = detection[5:]  # 各个类别的置信度
            classID = np.argmax(scores)  # 最高置信度的id即为分类id
            confidence = scores[classID]  # 拿到置信度

            # 根据置信度筛查
            if confidence > CONFIDENCE:
                box = detection[0:4] * np.array([W, H, W, H])  # 将边界框放会图片尺寸
                (centerX, centerY, width, height) = box.astype("int")
                x = int(centerX - (width / 2))
                y = int(centerY - (height / 2))
                boxes.append([x, y, int(width), int(height)])
                confidences.append(float(confidence))
                classIDs.append(classID)

    # # 2）应用非最大值抑制(non-maxima suppression，nms)进一步筛掉
    idxs = cv.dnn.NMSBoxes(boxes, confidences, CONFIDENCE, THRESHOLD) # boxes中，保留的box的索引index存入idxs
    # 得到labels列表
    with open(labelsPath, 'rt') as f:
        labels = f.read().rstrip('\n').split('\n')
    # 应用检测结果
    np.random.seed(42)
    COLORS = np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8")  # 框框显示颜色，每一类有不同的颜色，每种颜色都是由RGB三个值组成的，所以size为(len(labels), 3)
    if len(idxs) > 0:
        for i in idxs.flatten():  # indxs是二维的，第0维是输出层，所以这里把它展平成1维
            (x, y) = (boxes[i][0], boxes[i][1])
            (w, h) = (boxes[i][2], boxes[i][3])

            color = [int(c) for c in COLORS[classIDs[i]]]
            cv.rectangle(img, (x, y), (x+w, y+h), color, 2)  # 线条粗细为2px
            text = "{}: {:.4f}".format(labels[classIDs[i]], confidences[i])
            cv.putText(img, text, (x, y-5), cv.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)  # cv.FONT_HERSHEY_SIMPLEX字体风格、0.5字体大小、粗细2px
    cv2.imshow("YOLO-Fastest-XL Test", img) #Display image

    key = cv2.waitKey(10)
    c = chr(key & 255)
    if c in ['q', 'Q', chr(27)]:
        break

cv2.destroyWindow("test")
